Senior Director, Head of AI for Clinical Development, Late Oncology

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real. 
 

About AISI 

AI Science & Innovation (AISI) sits at the centre of AstraZeneca’s R&D AI transformation. Our remit is to build, buy and deliver the AI models and agents that change pipeline outcomes, across discovery, translational science, biomarkers and clinical development.  

Role Overview 

AstraZeneca is building a world-class AI capability for Clinical Development within AISI to accelerate the design, conduct, and analysis of confirmatory trials across our Oncology and BioPharmaceuticals pipeline. Phase III is where assets meet the regulatory and label-evidence bar — and where AI must be useful, defensible, and audit-ready. We are hiring the Head of AI for Clinical Development, Late Oncology, to lead the science and the team that turns AI promise into real-world improvements in late phase clinical trials, including trial enrichment, external evidence, label-relevant endpoints, and submission-ready insight.  

This is one of the hardest and highest impact challenges in AI for healthcare. The playbook for AI at this stage will be developed in the next two to three years. We partner closely with domain experts in clinical development, regulatory, and biometrics to advance the science of clinical development to bring better treatments to patients, faster, while adhering to the highest evidentiary standards. We’re hiring someone who sees that as the reason to come because they are committed to using AI for real, measurable improvement in healthcare. 

AI for clinical development is a field in motion. Foundation models, multimodal learning, agentic systems, and causal AI advance rapidly, and the regulatory and methodological frameworks around them are evolving in parallel. You’ll pick the right bets among rapidly changing options, and continuously absorb new methods as the field redefines what’s possible. Comfort with ambiguity and an instinct to learn in public are core to the role. 

The AI for Clinical Development function is being built from the ground up, and you’ll help define how AstraZeneca does AI for late-phase trials. Expect an outsized voice with regulators, scientific consortia, and external partners during the narrow window when the rules of the road for AI in pivotal evidence are being written. 

We hire for learning agility and technical excellence. The strongest candidate is the person who learns fast, is comfortable with ambiguity, prototypes early, fails forward, and partners credibly across communities (ML, clinical, biostatistics, regulatory). We’re seeking a scientist with the leadership, technical depth, and curiosity to develop, adapt, and apply the most advanced methods in AI, including multimodal foundation models, agentic AI, generative patient models, to late-phase clinical decision-making, and who can sit across from clinical-development teams to translate their priorities into actionable, innovative, and evaluable AI solutions. 

Above all, this is a role where the science matters. Every model you ship will eventually touch a trial that decides whether a patient gets a better therapy. That is the bar we hold ourselves to, and the bar we hire to. 
 

What you'll do 

• Lead AstraZeneca’s AI R&D for Phase III programs across oncology, leveraging and integrating AI solutions across the late development program. 
• Lead a team of AI researchers and engineers to leverage and integrate AI solutions across late oncology clinical development. 
• Develop and evaluate AI methods for innovative trial design (e.g., prognostic risk modelling, predictive enrichment, AI-driven stratification, sample-size and power optimisation, and biomarker-defined population strategy). 
• Lead innovative approaches to analyze and quality control real-time clinical trial data from late phase trials. 
• Develop and evaluate predictive models, including AI-based analysis of real-world evidence for Phase III trials (e.g., external control arm methodology, predictive and progrnostic models, generative patient trajectory models). 
• Develop and evaluate AI methods for multimodal label-relevant endpoints — im